Crude oil, an indispensable commodity, is central to various industries, from agriculture to pharmaceuticals. The global oil trade mainly revolves around two benchmark grades: West Texas Intermediate (WTI) and Brent. WTI, extracted predominantly in Texas, is notable for its low sulfur content and ease of refinement due to its lightness and sweetness. Conversely, Brent, sourced from the North Sea, also possesses low sulfur and density, making it desirable for easy refining.

Analyzing and trading these benchmarks involves understanding their pricing dynamics. The price differential, or spread, between Brent and WFI can be indicative of various economic factors. Leveraging machine learning techniques to model and predict this spread could offer significant forecasting advantages. By employing supervised machine learning, traders can develop models that predict future price movements ba...

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Understanding the key functions of automated trading strategies is central to optimizing performance in financial markets. This system is designed to initiate buy or sell orders based on price action analysis, ensuring decisions are made on real-time market movements. Additionally, the strategy enhances risk management by automatically calculating the optimal lot size for each trade, tailoring investment proportionate to account balance and market conditions.

Further refining trade execution, the system employs an Automatic Trailing Stop adjusted by the Average True Range (ATR), allowing trades to lock in profits while adapting to volatility. This feature is essential for managing the risks associated with price fluctuations and securing potential gains without manual intervention.

The robustness of this automated strategy can be assessed through backtesting, where historical data i...

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In the dynamic realm of trading technology, developers continue to focus on automation and efficiency. Recent developments have introduced new functions that can significantly simplify the trading process. Key features now include the automatic calculation of lot sizes and the implementation of ATR-based trailing stops, which help in optimizing trade exits based on market volatility.

Furthermore, the integration of volume and moving average indicators into trading algorithms allows for more data-driven decision-making regarding entry and exit points. These technical tools facilitate the identification of market conditions that favor either buying or selling.

For those involved in the development and testing of trading systems, the backbone of such enhancements rests in the robust coding of these functions. The declaration of volume indicators and the design of tick functions are fun...

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The recent development in automated trading focuses on optimizing the decision-making process by executing trades based on specific price actions. This automation includes calculating the appropriate lot size dynamically and implementing an automatic trailing stop that is adjusted according to the Average True Range (ATR) indicator. These features combine to enhance trade management and risk assessment capabilities.

Additionally, the effectiveness of these functions is verifiable through comprehensive backtesting results, providing insights into the predictive accuracy and reliability of the strategy under historical market conditions.

The core of this automation lies within three main functions: managing trailing stops and position counts, calculating lot sizes accurately, and executing trades via the main tick function. This structured approach allows for meticulous control over t...

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Algorithmic traders often seek robust functionalities in trading platforms to enhance their strategy efficiency. A vital capability includes the main function for calculating lot sizes which ensures appropriate risk management tailored to the trader's specific risk appetite and account balance. Enhancing this feature are trailing stops and position counting functionalities that provide dynamic management of open positions to lock in profits and prevent excessive losses.

Another critical function is enabling trading commands to only execute at the opening of a new candle, maximizing the relevance of technical analysis signals. Additionally, the ability to automatically draw support and resistance levels gives traders visual cues for better decision-making directly on their charts.

Overall, incorporating these functionalities into trading scripts fundamentally aids in creating more pr...

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In the intricate world of secure trading, predicting the movement of currency pairs is essential. A new method titled "Latént Variable Sequential Set Transformers" (AutoBots), offering a promising solution, is based on the Encoder-Decoder architecture. This model generates sequences of trajectories for multiple agents, applicable in both robotic system control and financial markets.

The AutoBots model processes sequences of environmental states across time steps, infusing each with temporal and contextual information via transformations in its Encoder. This integration allows the model to predict future trajectories of agents, adapting to dynamic scenario conditions typical in market environments.

The Decoder aspect of AutoBots extends its utility by generating socially and temporally consistent predictions about multiple future scenarios from the same initial scene data. This metho...

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A new indicator has been developed to identify support and resistance levels on trading charts, tailored for traders who need precise tools for their trading strategies. The unique aspect of this indicator is its method of determining the highest and lowest price points over a specified window of time, thereby marking potential support and resistance zones.

The indicator operates by comparing price points over two overlapping ranges: the primary 'Period' and an extended 'Overlook' period. For instance, if configured with a 'Period' of 20 and an 'Overlook' of 10, it evaluates the highest and lowest prices within these intervals to confirm true peaks and troughs before plotting them on the chart.

Additionally, the indicator ensures consistency by maintaining displayed levels as long as the current price remains between the identified support and resistance. This could be particularly...

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The latest update of MetaTrader 5 build 4380 introduces important improvements and fixes:

• Added Alt+X hotkey for experts list and fixed potential Live Update issue in the desktop platform
• Fixed Bitmap object bugs in the strategy tester
• Introduced new requirements for the MQL5 Cloud Network: agents running in virtual environments and with no AVX support are not accepted anymore
• Improved one click trading panel on the chart in the web platform

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In modern trading, multiple methods are utilized for building trading systems, categorized into graphical analysis, computing systems, and news systems. Each method has its own specific tools and approaches. Graphical analysis, favored for its simplicity in placing orders based on visible chart patterns, allows traders to create visual reference points based on price and time.

Computing systems use a range of automated tools like EAs, harnessing indicators and neural algorithms to inform trading decisions. News systems analyze broad market trends through global news and insider information, influencing trading strategies.

Traders commonly select one primary method while employing others as supplementary tools. Combining these methods can provide a comprehensive view of market conditions, aiding in more informed decision-making processes in trading strategies. Efficient trading rel...

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This Expert Advisor (EA) operates as a sample multicurrency system for Metatrader 5, utilizing basic candlestick logic on daily charts (D1) to determine entry points. The simplicity of its design is intended to assist other developers in learning and understanding the fundamentals of EA construction and application in financial trading environments. This tool is especially useful for those looking to implement and test multicurrency strategies in a simulated or live trading scenario.

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In the evolving realm of algorithmic trading, the emphasis on efficiency has propelled the utilization of optimization algorithms to a crucial status. These algorithms are key to developing trading strategies that not only meet desired standards of profitability but also minimize risks. Central to the practical application of such optimizations is the concept of self-optimization in Expert Advisors (EA). This process entails the adaptation of trading strategy parameters in response to market changes, optimizing for elements like profit maximization or risk reduction.

An effective system for EA self-optimization involves multiple steps starting with extensive data collection and analysis, followed by goal setting and the application of appropriate optimization algorithms. Post-optimization, rigorous backtesting and validation ensure the strategy's ongoing relevancy against current mar...

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Conditional Generative Adversarial Networks (cGANs) offer a refined approach in neural network training where both the generator and discriminator are conditioned on external data. This variant deviates from typical GANs by using labeled data to steer the generative process, enhancing the application range from image synthesis to sophisticated tasks like financial time series forecasting.

Unlike unconditioned GANs that generate outputs from random noise, cGANs utilize specific input types that align closely with the training data, thus producing more targeted and relevant outputs. This specificity is invaluable in fields where the type of generated data needs to conform closely to existing patterns, such as in financial markets where precision is crucial.

The technical setup for cGAN typically includes a main generator network that creates data based on conditioned inputs, and a dis...

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The Relative Strength Index (RSI), a pivotal tool initially formulated by John Welles Wilder Jr. in his 1978 publication, "New Concepts in Technical Trading Systems," has been adeptly adapted for enhanced computational efficiency while retaining identical mathematical outcomes. The adapted code supports conditional compilation, enabling compatibility with both MQL4 and MQL5 environments. For those interested in further examination or usage, the source code for this and other similar implementations can now be found under the "Public Projects" section of MetaEditor, labeled "FMIC." This initiative provides developers with easily accessible tools to integrate and implement in their trading systems.

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In advancing the development of expert advisors (EAs) for trading platforms like MetaTrader 5, the integration of manual controls within automated systems remains a topic of significant importance. The interaction between automated strategies and manual intervention can be finely tuned by employing specific programming techniques. This approach ensures that traders can still exert personal control over the trading process when necessary.

For programmers transitioning from general programming to the MQL5 environment, understanding how to manipulate order limits and implement them visually on trading charts is crucial. Especially when these orders are placed manually within an EA context, it is essential to display such limits clearly. This is not just about enhancing usability but also about maintaining the integrity of the trading strategy by preventing accidental order placements.
...

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The Relative Strength Index (RSI), a robust tool created by John Welles Wilder Jr. and detailed in his 1978 book "New Concepts in Technical Trading Systems," is available in a new implementation. The updated version of this indicator features a revised equation that enhances computational efficiency while maintaining the original mathematical integrity. Additionally, the code is compatible with both MQL4 and MQL5 thanks to conditional compilation. For interested developers, the source code for this and other tools can be found in the "Public Projects" section of MetaEditor, labeled under "FMIC." This resource is particularly useful for those involved in financial markets and algorithmic trading.

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In the realm of automated trading, understanding triggers—though complex—is crucial for developing effective trading strategies using Expert Advisors (EAs). Triggers, which signal various trading actions such as opening or closing positions, are integral in navigating the volatile trading environment but they come with inherent risk. Issues of reliability make it imperative to approach triggers with caution to avoid missed opportunities or substantial losses.

Transitioning from fundamental order systems discussed previously, the attention now shifts to advanced implementation techniques for EAs, specifically focusing on manual interaction and event-driven strategies. Among these methods, manipulating mouse and keyboard events to generate trading orders presents its own set of challenges. The simplicity of such methods is deceptive; they necessitate a high level of precision in coding...

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An implementation of the original Average True Range (ATR) indicator, as developed by John Welles Wilder Jr. and detailed in his 1978 book, "New Concepts in Technical Trading Systems," is now available. This version employs the Wilder’s Moving Average (SMMA) rather than the Simple Moving Average (SMA) commonly used in standard ATR indicators. Here, the period set by default is 7 days, aligning with Wilder's original specifications instead of the commonly used 14-day period.

The indicator is compatible with both MQL4 and MQL5, thanks to the use of conditional compilation. Additionally, the source code for this and other related tools can be found under the 'Public Projects' section in the MetaEditor, grouped under the project name "FMIC." This allows developers and technical analysts to access and integrate these tools in their trading systems effectively.

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The invasive weed metaheuristic algorithm stands as a notable technique in the field of optimization algorithms. Originating from the resilience and adaptability of weed species, this approach simulates weed population dynamics to solve optimization problems effectively. This algorithm is especially relevant for scenarios where solutions need exploration across a broad and varied landscape, akin to weeds spreading across a field.

In technical terms, this algorithm initializes with a random distribution of 'seeds'—potential solutions—across the problem space. Each seed grows into a 'weed', where its fitness is calculated based on the optimization criteria. Weeds with higher fitness scores produce more seeds, thus propagating their traits more extensively in the next generation. The process iterates, continually refining solutions until optimal or near-optimal solutions emerge.

This m...

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The indicator presented provides a precise implementation of John Welles Wilder Jr.'s Original Average True Range (ATR) from his seminal 1978 publication, New Concepts in Technical Trading Systems. It utilizes Wilder's moving average, specifically the smoothed moving average (SMMA), for greater accuracy compared to the commonly used simple moving average (SMA).

Notably, this indicator is configured with a default period of 7 rather than the traditional 14, aligning closely with Wilder's specifications. The coding is versatile, allowing compilation in both MQL4 and MQL5 environments, ensuring broad compatibility.

For developers interested in accessing the source code, it is now available under the "Public Projects" tab of MetaEditor, labeled as “FMIC”. This resource offers a valuable tool for those looking to integrate a foundational technical analysis element into their trading stra...

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Machine learning in electronic trading has evolved to enhance strategy performance, particularly through focusing on technical indicator values rather than direct price forecasts. A study underscores the limitations of achieving over 50% accuracy in security price prediction. By switching the focus to technical indicators, accuracy can improve up to approximately 70%. This approach utilizes comprehensive data fetched and analyzed using Python and MetaTrader 5, demonstrating a more reliable method by fully accounting for variables influencing technical indicators.

Analysis reveals a stark contrast in the effectiveness of predicting price movements versus technical indicators. Modelling focuses on factors fully observable within technical indicators, fundamentally boosting prediction accuracy. This methodology leverages historical data processing and Principal Components Analysis (PCA)...

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